Weekly Exercises #6

library(tidyverse)     # for data cleaning and plotting

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## v tibble  3.0.5     v dplyr   1.0.3
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.0

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(gardenR)       # for Lisa's garden data
library(lubridate)     # for date manipulation

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## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
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##     date, intersect, setdiff, union

library(openintro)     # for the abbr2state() function

## Loading required package: airports

## Loading required package: cherryblossom

## Loading required package: usdata

library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data

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library(ggmap)         # for mapping points on maps

## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

library(gplots)        # for col2hex() function

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library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data

## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

library(leaflet)       # for highly customizable mapping
library(ggthemes)      # for more themes (including theme_map())
library(plotly)        # for the ggplotly() - basic interactivity

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library(gganimate)     # for adding animation layers to ggplots
library(gifski)        # for creating the gif (don't need to load this library every time,but need it installed)
library(transformr)    # for "tweening" (gganimate)

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library(shiny)         # for creating interactive apps
library(patchwork)     # for nicely combining ggplot2 graphs  
library(gt)            # for creating nice tables

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## Attaching package: 'gt'

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library(rvest)         # for scraping data

## Loading required package: xml2

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##     html

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library(robotstxt)# for checking if you can scrape data
library(paletteer)

## Warning: package 'paletteer' was built under R version 4.0.4

theme_set(theme_minimal())

# Lisa's garden data
data("garden_harvest")

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   date = col_date(format = ""),
##   state = col_character(),
##   fips = col_character(),
##   cases = col_double(),
##   deaths = col_double()
## )

Put your homework on GitHub!

Go here or to previous homework to remind yourself how to get set up.

Once your repository is created, you should always open your project rather than just opening an .Rmd file. You can do that by either clicking on the .Rproj file in your repository folder on your computer. Or, by going to the upper right hand corner in R Studio and clicking the arrow next to where it says Project: (None). You should see your project come up in that list if you’ve used it recently. You could also go to File –> Open Project and navigate to your .Rproj file.

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• NEW!! With animated graphs, add eval=FALSE to the code chunk that creates the animation and saves it using anim_save(). Add another code chunk to reread the gif back into the file. See the tutorial for help.

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Your first shiny app

1. This app will also use the COVID data. Make sure you load that data and all the libraries you need in the app.R file you create. Below, you will post a link to the app that you publish on shinyapps.io. You will create an app to compare states’ cumulative number of COVID cases over time. The x-axis will be number of days since 20+ cases and the y-axis will be cumulative cases on the log scale (scale_y_log10()). We use number of days since 20+ cases on the x-axis so we can make better comparisons of the curve trajectories. You will have an input box where the user can choose which states to compare (selectInput()) and have a submit button to click once the user has chosen all states they’re interested in comparing. The graph should display a different line for each state, with labels either on the graph or in a legend. Color can be used if needed.

#I couldn't figure out how to get shinyapp to work so I just made the graph. Sorry
#I added a ggplotly so it's easier for you to tell which line is which
state_graph <- covid19 %>% 
  group_by(state, date, cases) %>%
  filter(cases >= 20) %>%
  group_by(state) %>%
  mutate(min_month_date = min(date)) %>%
  mutate(num_cases_since_20 = date - min_month_date) %>%
  ggplot(aes(x = num_cases_since_20,
    y = log10(cases),
    color = state)) +
  geom_line() +
  labs(title = "Cumulative Covid19 Cases Over Time Per State",
       x = "",
       y = "")

ggplotly(state_graph)

## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.

Warm-up exercises from tutorial

2. Read in the fake garden harvest data. Find the data here and click on the Raw button to get a direct link to the data.

fake_garden_harv <- read.csv("https://raw.githubusercontent.com/llendway/scraping_etc/main/2020_harvest.csv")

3. Read in this data from the kaggle website. You will need to download the data first. Save it to your project/repo folder. Do some quick checks of the data to assure it has been read in appropriately.

groceries <- read.csv("Groceries_dataset.csv")
dim(groceries)

## [1] 38765     3

4. CHALLENGE(not graded): Write code to replicate the table shown below (open the .html file to see it) created from the garden_harvest data as best as you can. When you get to coloring the cells, I used the following line of code for the colors argument:

colors = scales::col_numeric(
      palette = paletteer::paletteer_d(
        palette = "RColorBrewer::YlGn"
      ) %>% as.character()
colors = scales::col_numeric(palette = paletteer::paletteer_d())
#![](garden_table.html){width=600, height=1000}

#for assigning colors

jul_colors = scales::col_numeric(
      palette = paletteer::paletteer_d(
        palette = "RColorBrewer::YlGn") %>%
  as.character(),
  domain = c(0.1, 13))

aug_colors = scales::col_numeric(
      palette = paletteer::paletteer_d(
        palette = "RColorBrewer::YlGn") %>% 
  as.character(),
  domain = c(0.1, 32))

sep_colors = scales::col_numeric(
      palette = paletteer::paletteer_d(
        palette = "RColorBrewer::YlGn") %>% 
  as.character(),
  domain = c(0.1, 28))

oct_colors = scales::col_numeric(
      palette = paletteer::paletteer_d(
        palette = "RColorBrewer::YlGn") %>% 
  as.character(),
  domain = c(0.1, 12))

garden_harvest %>% 
  mutate(month = month(date, label = TRUE)) %>% 
  filter(vegetable %in% c("beans", "carrots", "tomatoes")) %>% 
  group_by(vegetable, variety, month) %>% 
  summarise(weight_lbs = sum(weight)*0.00220462) %>% 
  pivot_wider(id_cols = vegetable:weight_lbs,
              names_from = "month",
              values_from = "weight_lbs") %>% 
  replace_na(list(Jul = 0, Aug = 0, Sep = 0, Oct = 0)) %>% 
  gt(rowname_col = "variety",
    groupname_col = "vegetable") %>% 
  tab_header(title = "Bean, Carrot, and Tomato Harvest (lb)") %>%
  fmt_number(columns = c("Jul", "Aug", "Sep", "Oct"),
             decimals = 2) %>%
  summary_rows( #groups = TRUE,
    columns = c("Jul", "Aug", "Sep", "Oct"),
    fns = list(sum = "sum"),
    formatter = fmt_number,
    decimals = 1) %>% 
  data_color(columns = vars(Jul),
             colors = jul_colors) %>% 
  data_color(columns = vars(Aug),
             colors = aug_colors) %>% 
  data_color(columns = vars(Sep),
             colors = sep_colors) %>% 
  data_color(columns = vars(Oct),
             colors = oct_colors)

## `summarise()` has grouped output by 'vegetable', 'variety'. You can override using the `.groups` argument.

## Warning: Some values were outside the color scale and will be treated as NA

## Warning: Some values were outside the color scale and will be treated as NA

## Warning: Some values were outside the color scale and will be treated as NA

## Warning: Some values were outside the color scale and will be treated as NA

## Warning: The `.dots` argument of `group_by()` is deprecated as of dplyr 1.0.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Bean, Carrot, and Tomato Harvest (lb)
	Jul	Aug	Sep	Oct
beans
Bush Bush Slender	12.89	9.04	0.21	0.00
Chinese Red Noodle	0.00	0.78	0.00	0.00
Classic Slenderette	0.00	3.07	0.53	0.00
carrots
Bolero	0.26	3.37	1.36	3.31
Dragon	0.18	1.98	1.95	0.00
greens	0.00	0.37	0.00	0.00
King Midas	1.49	0.00	0.35	2.26
tomatoes
Amish Paste	1.92	31.26	22.31	10.19
Better Boy	2.79	13.88	14.76	2.58
Big Beef	0.75	12.66	8.55	3.04
Black Krim	0.00	14.46	0.52	0.83
Bonny Best	1.72	15.84	6.51	0.85
Brandywine	0.00	11.81	2.91	0.92
Cherokee Purple	1.75	13.04	0.44	0.48
grape	1.34	15.87	12.15	3.04
Jet Star	0.69	7.60	5.96	0.76
Mortgage Lifter	1.77	12.14	9.05	3.36
Old German	2.74	9.25	10.88	3.85
volunteers	0.00	12.57	27.78	11.26
sum	30.3	189.0	126.2	46.7

5. Create a table using gt with data from your project or from the garden_harvest data if your project data aren’t ready.

garden_harvest_table <- garden_harvest %>%
  filter(vegetable == "lettuce") %>% 
  gt(rowname_col = "variety",
     groupname_col = "vegetable") %>% 
  tab_header(title = md("**Table of Lettuce From Garden Harvest Dataset**"))
garden_harvest_table

Table of Lettuce From Garden Harvest Dataset
	date	weight	units
lettuce
reseed	2020-06-06	20	grams
reseed	2020-06-08	15	grams
reseed	2020-06-09	10	grams
Farmer's Market Blend	2020-06-11	12	grams
Farmer's Market Blend	2020-06-13	19	grams
Farmer's Market Blend	2020-06-17	48	grams
Farmer's Market Blend	2020-06-18	47	grams
Farmer's Market Blend	2020-06-19	39	grams
Farmer's Market Blend	2020-06-19	38	grams
Farmer's Market Blend	2020-06-20	22	grams
Tatsoi	2020-06-20	18	grams
Farmer's Market Blend	2020-06-21	95	grams
Farmer's Market Blend	2020-06-22	52	grams
Farmer's Market Blend	2020-06-22	18	grams
Farmer's Market Blend	2020-06-24	122	grams
Farmer's Market Blend	2020-06-25	30	grams
Farmer's Market Blend	2020-06-27	52	grams
Tatsoi	2020-06-27	89	grams
Farmer's Market Blend	2020-06-28	111	grams
Farmer's Market Blend	2020-06-29	58	grams
mustard greens	2020-06-29	23	grams
Farmer's Market Blend	2020-06-29	82	grams
Farmer's Market Blend	2020-07-01	60	grams
Tatsoi	2020-07-02	144	grams
Farmer's Market Blend	2020-07-03	217	grams
Tatsoi	2020-07-03	216	grams
Farmer's Market Blend	2020-07-04	147	grams
Tatsoi	2020-07-06	189	grams
Farmer's Market Blend	2020-07-07	67	grams
Farmer's Market Blend	2020-07-07	13	grams
Farmer's Market Blend	2020-07-08	39	grams
Tatsoi	2020-07-08	75	grams
Farmer's Market Blend	2020-07-09	61	grams
Farmer's Market Blend	2020-07-11	79	grams
Farmer's Market Blend	2020-07-12	83	grams
Farmer's Market Blend	2020-07-13	53	grams
Tatsoi	2020-07-13	137	grams
Farmer's Market Blend	2020-07-16	61	grams
Tatsoi	2020-07-20	123	grams
Lettuce Mixture	2020-07-22	23	grams
Lettuce Mixture	2020-07-23	130	grams
Lettuce Mixture	2020-07-24	16	grams
Lettuce Mixture	2020-07-26	81	grams
Lettuce Mixture	2020-07-27	99	grams
Lettuce Mixture	2020-07-28	91	grams
Lettuce Mixture	2020-07-29	73	grams
Lettuce Mixture	2020-07-30	94	grams
Lettuce Mixture	2020-07-31	107	grams
Lettuce Mixture	2020-08-03	65	grams
Lettuce Mixture	2020-08-04	56	grams
Lettuce Mixture	2020-08-06	98	grams
Lettuce Mixture	2020-08-07	19	grams
Lettuce Mixture	2020-08-11	92	grams
Lettuce Mixture	2020-08-12	73	grams
Lettuce Mixture	2020-08-14	80	grams
Lettuce Mixture	2020-08-15	56	grams
Lettuce Mixture	2020-08-16	45	grams
Lettuce Mixture	2020-08-17	67	grams
Lettuce Mixture	2020-08-18	87	grams
Lettuce Mixture	2020-08-20	99	grams
Tatsoi	2020-08-20	322	grams
Lettuce Mixture	2020-08-23	111	grams
Lettuce Mixture	2020-08-24	134	grams
Lettuce Mixture	2020-08-27	14	grams
Lettuce Mixture	2020-08-28	85	grams
Lettuce Mixture	2020-09-16	8	grams
Lettuce Mixture	2020-09-26	95	grams
Lettuce Mixture	2020-09-27	139	grams
Lettuce Mixture	2020-10-07	17	grams

6. Use patchwork operators and functions to combine at least two graphs using your project data or garden_harvest data if your project data aren’t read.

g1 <- garden_harvest %>% 
  filter(vegetable == "radish") %>% 
  ggplot(aes(x = date,
             y = weight)) +
  geom_line() +
  labs(title = "Weight of Radishes Over Time",
       x = "", 
       y = "")
  
  
g2 <- garden_harvest %>% 
  filter(vegetable == "beets") %>% 
  ggplot(aes(x = date,
             y = weight)) +
  geom_line() +
  labs(title = "Weight of Beets Over Time",
       x = "",
       y = "")


g1 + g2

GitHub Link

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?